In audio and video productions, various audio-related problems are manually detected and removed. In audio recordings, for example, system, background and ambient noises are often removed or reduced. System noise can result, for example, from the use of batch transistors, coupled system components, amplification, automatic gain control, and/or other hardware component characteristics. Ambient noises can result from strong winds, recording in a moving vehicle, and other circumstances in which there are surrounding influences. Breath sounds, another type of audio-related problem, may result given a particular speaker's speech characteristics and may be unintentionally amplified in the producing of an audio recording. Other audio-related problems arise as a result of changing recording levels. The recording level may vary, for example, from take to take of a video production, based on differing microphone locations relative to a speaker, and due to various other causes.
Such audio-related problems can be detected and corrected in various ways depending on the type and nature of the noise. Some noise can be detected by analyzing the spectral shape to find a portion of very low noise that is present when nobody is speaking or when a strong signal is not present and then using that as a reference to detect noises that can be removed or reduced consistently or in varying ways throughout an audio recording. Certain noises can be corrected by identifying a fingerprint. For example, if a recording has ventilation noises, a portion of the audio in which such noises are distinct can be selected as a fingerprint that is then used to find and correct similar noises throughout the recording. Correction of audio problems often involves manually identifying a portion of audio, for example, where a breath sound occurs, to make some adjustment, for example, to decrease of level so that the breath sound is not audible. To address differences in level caused by two speakers at different distances from a microphone, a useruser may amplify the recording whenever one of the speakers is speaking Audio problems can also be corrected via a technique sometimes referred to as “colorization” or “equalization” in which a useruser amplifies or reduces certain spectral areas. For example, such a technique may be used when mixing two recordings, where one has rain sounds from surrounding rain.
Audio-related problems are generally manually detected by a useruser listening to an audio stream or audio recording and manually identifying problematic portions. The same or another useruser goes through all those areas and attempts to fix them. Such manual listening, identification, correction processes are time consuming, difficult to speed up, and often involve multiple cycles or iterations. In contrast to manual processes which are often burdensome and time-consuming, automated audio problem detection processes fail to allow sufficient user control of the process. For example, some mastering studios offer web-services to which a useruser can send in audio tracks that are opaquely processed by the company and sent back to the customer. Such services do not allow the user to adequately control the process, for example, by identifying problems that should or not be fixed and/or selection of an appropriate fix for a given problem. Some audio applications have tools for scanning audio and displaying artifacts. However, such tools require significant user interaction and require setting of complex parameters and settings. Generally, existing techniques addressing audio problems generally are excessively time consuming and burdensome, lack adequate user control, or both.